There is a consensus that visual working memory (WM) resources are sharply limited, but debate persists regarding the simple question of whether there is a limit to the total number of items that can be stored concurrently. Zhang and Luck (2008) advanced this debate with an analytic procedure that provided strong evidence for random guessing responses, but their findings can also be described by models that deny guessing while asserting a high prevalence of low precision memories. Here, we used a whole report memory procedure in which subjects reported all items in each trial, and indicated whether they were guessing with each response. Critically, this procedure allowed us to measure memory performance for all items in each trial. When subjects were asked to remember 6 items, the response error distributions for about 3 out of the 6 items were best fit by a parameter-free guessing model (i.e. a uniform distribution). In addition, subjects’ self-reports of guessing precisely tracked the guessing rate estimated with a mixture model. Control experiments determined that guessing behavior was not due to output interference, and that there was still a high prevalence of guessing when subjects were instructed not to guess. Our novel approach yielded evidence that guesses, not low-precision representations, best explain limitations in working memory. These guesses also corroborate a capacity-limited working memory system – we found evidence that subjects are able to report non-zero information for only 3–4 items. Thus, WM capacity is constrained by an item limit that precludes the storage of more than 3–4 individuated feature values.

Persistent neuronal spiking has long been considered the mechanism underlying working memory, but recent proposals argue for alternative, “activity-silent” substrates. Using monkey and human electrophysiology, we show here that attractor dynamics that control neural spiking during mnemonic periods interact with activity-silent mechanisms in PFC. This interaction allows memory reactivations, which enhance serial biases in spatial working memory. Stimulus information was not decodable between trials, but remained present in activity-silent traces inferred from spiking synchrony in PFC. Just prior to the new stimulus, this latent trace was reignited into activity that recapitulated the previous stimulus representation. Importantly, the reactivation strength correlated with the strength of serial biases in both monkeys and humans, as predicted by a computational model integrating activity-based and activity-silent mechanisms. Finally, single-pulse TMS applied to the human prefrontal cortex between successive trials enhanced serial biases, demonstrating the causal role of prefrontal reactivations in determining working memory behavior.

Attentional control and working memory capacity are important cognitive abilities that substantially vary between individuals. Although much is known about how attentional control and working memory capacity relate to each other and to constructs like fluid intelligence, little is known about how trial-by-trial fluctuations in attentional engagement impact trial-by-trial working memory performance. Here, we employ a novel whole-report memory task that allowed us to distinguish between varying levels of attentional engagement in humans performing a working memory task. By characterizing low-performance trials, we can distinguish between models in which working memory performance failures are caused by either (1) complete lapses of attention or (2) variations in attentional control. We found that performance failures increase with set-size and strongly predict working memory capacity. Performance variability was best modeled by an attentional control model of attention, not a lapse model. We examined neural signatures of performance failures by measuring EEG activity while participants performed the whole-report task. The number of items correctly recalled in the memory task was predicted by frontal theta power, with decreased frontal theta power associated with poor performance on the task. In addition, we found that poor performance was not explained by failures of sensory encoding; the P1/N1 response and ocular artifact rates were equivalent for high- and low-performance trials. In all, we propose that attentional lapses alone cannot explain individual differences in working memory performance. Instead, we find that graded fluctuations in attentional control better explain the trial-by-trial differences in working memory that we observe.

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Complex cognition relies on both on-line representations in working memory (WM), said to reside in the focus of attention, and passive off-line representations of related information. Here, we dissected the focus of attention by showing that distinct neural signals index the on-line storage of objects and sustained spatial attention. We recorded electroencephalogram (EEG) activity during two tasks that employed identical stimulus displays but varied the relative demands for object storage and spatial attention. We found distinct delay-period signatures for an attention task (which required only spatial attention) and a WM task (which invoked both spatial attention and object storage). Although both tasks required active maintenance of spatial information, only the WM task elicited robust contralateral delay activity that was sensitive to mnemonic load. Thus, we argue that the focus of attention is maintained via a collaboration between distinct processes for covert spatial orienting and object-based storage.